A gaussian groundplan projection area model for evolving probabilistic classifiers

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Theodoros Theodoridis and Alexandros Agapitos and 
                 Huosheng Hu",
  title =        "A gaussian groundplan projection area model for
                 evolving probabilistic classifiers",
  booktitle =    "GECCO '11: Proceedings of the 13th annual conference
                 on Genetic and evolutionary computation",
  year =         "2011",
  editor =       "Natalio Krasnogor and Pier Luca Lanzi and 
                 Andries Engelbrecht and David Pelta and Carlos Gershenson and 
                 Giovanni Squillero and Alex Freitas and 
                 Marylyn Ritchie and Mike Preuss and Christian Gagne and 
                 Yew Soon Ong and Guenther Raidl and Marcus Gallager and 
                 Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and 
                 Nikolaus Hansen and Silja Meyer-Nieberg and 
                 Jim Smith and Gus Eiben and Ester Bernado-Mansilla and 
                 Will Browne and Lee Spector and Tina Yu and Jeff Clune and 
                 Greg Hornby and Man-Leung Wong and Pierre Collet and 
                 Steve Gustafson and Jean-Paul Watson and 
                 Moshe Sipper and Simon Poulding and Gabriela Ochoa and 
                 Marc Schoenauer and Carsten Witt and Anne Auger",
  isbn13 =       "978-1-4503-0557-0",
  pages =        "1339--1346",
  keywords =     "genetic algorithms, genetic programming",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  DOI =          "doi:10.1145/2001576.2001757",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "In this paper, an investigation of evolvable
                 probabilistic classifiers is conducted, along with a
                 thorough comparison between a classical Gaussian
                 distance model, and the induction of Gaussian-to-circle
                 projection model. The newly introduced model refers to
                 a distance fitness measure, based on the projection of
                 Gaussian distributions with geometric circles. The
                 projection architecture aims to model and classify
                 physical aggressive behaviours, by using biomechanical
                 primitives. The primitives are being used to model the
                 dynamics of the aggressive activities, by evolving
                 biomechanical classifiers, which can discriminate
                 between three behaviours and six actions. Both
                 evolutionary models have shown strong discrimination
                 performances on recognising the individual actions of
                 each behaviour. From the comparison, the proposed model
                 outperformed the classical one with three ensemble
  notes =        "Also known as \cite{2001757} GECCO-2011 A joint
                 meeting of the twentieth international conference on
                 genetic algorithms (ICGA-2011) and the sixteenth annual
                 genetic programming conference (GP-2011)",

Genetic Programming entries for Theodoros Theodoridis Alexandros Agapitos Huosheng Hu